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题名: A hybrid EMG model for the estimation of multijoint movement in activities of daily living
作者: Ding QC(丁其川); Zhao XG(赵新刚); Han JD(韩建达)
作者部门: 机器人学研究室
会议名称: 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
会议日期: September 28-30, 2014
会议地点: Beijing, China
会议录: Processing of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014
会议录出版者: IEEE
会议录出版地: Piscataway, NJ, USA
出版日期: 2014
页码: 1-6
收录类别: EI ; CPCI(ISTP)
ISBN号: 978-1-4799-6731-5
关键词: surface electromyography ; Human-Robot Interface ; motion estimation ; pattern recognition
摘要: Accurately identifying human's intent of motion from electromyography (EMG) signals is the key to implement EMG-based HRI (Human-Robot Interface) systems. Human's intent of motion includes motion modes and continuous movement variables. In this paper, a hybrid EMG-to-motion model is constructed by combining a classification model and a regression model. Based on a proper division for joints, the classification model is utilized to recognize the motion modes of 'small' joints; meanwhile, the regression model is utilized to estimate the continuous movement variables of 'big' joints. Furthermore, a Bayesian network (BN) model, which sufficiently employs context information of a task, is also involved into the hybrid model to improve its performances for motion estimation. Experiments have been conducted with three subjects to demonstrate the feasibility of the proposed methods. In these experiments, the motion modes of hand and wrist, and the continuous elbow angles are estimated with sEMG signals considering a 'drinking' task. Finally, an upper limb prosthetic is controlled to simulate human's movement in a 'drinking' task. © 2014 IEEE.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/15684
Appears in Collections:机器人学研究室_会议论文

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